Rapid mass movement events in forested mountain terrain are frequently under-documented due to limited ground access, particularly in the immediate aftermath of the triggering episode. The landslide of 26 January 2024 in Krasnenska Forestry, Dubivska hromada (Tyachiv district, Zakarpattia Region, Ukrainian Carpathians) exemplifies this challenge: the event mobilised a large debris mass on a steep flysch slope, causing significant damage to forest stands and infrastructure, yet quantitative characterisation of its geometry and spatial extent required remote sensing analysis. This study presents a cloud-based, reproducible workflow for rapid landslide detection and area quantification implemented entirely within Google Earth Engine (GEE), combining Sentinel-1 Synthetic Aperture Radar (SAR) backscatter change detection with Sentinel-2 Normalised Difference Vegetation Index (NDVI) difference mapping.
The analysis zone was constrained to a 500 m buffer around roads within slopes exceeding 5° – a spatial filter designed to focus detection on geomorphologically susceptible terrain with infrastructure exposure. SAR change detection applied dual-channel thresholding (ΔVV < −1.5 dB and ΔVH < −1.5 dB) on pre-event (December 2023 – January 2024) and post-event (January – March 2024) Sentinel-1 IW descending-orbit acquisitions. Vegetation disturbance was quantified via dNDVI > 0.15 between a cloud-free pre-event scene (2023-08-29) and a post-event scene (2024-06-21). Spatially intersected SAR and NDVI change masks were vectorised and filtered by minimum area threshold. Slope-corrected surface areas were computed using SRTM-derived cosine correction. Ground-truth validation was provided by the State Enterprise "Forests of Ukraine", confirming the presence and approximate location of the landslide body on the identified slope section. The largest detected landslide body reached a horizontal projected area of 3.64 ha and a slope-corrected surface area of 3.86 ha.
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